In The Context of Creating Software, What Exactly Is Root Cause Analysis?

In The Context of Creating Software, What Exactly Is Root Cause Analysis?

Finding the underlying reasons behind a problem is the goal of root cause analysis (RCA). However, rather than focusing on a single culprit, our approach considers the complete causal chain.

​​Many businesses, especially those that rely heavily on technology, have found success with root cause analysis as a management tool. To use this method is to treat the patient as a doctor would. After recognizing symptoms, he moves on to more basic diagnostic procedures (such as blood testing) to determine the source of the illness. When the underlying cause of an illness is unclear, sophisticated diagnostic tools like computed tomography (CT) scans are employed by medical professionals. Through this process, he is able to zero in on the underlying issue.

When we apply this strategy to other sectors, we follow the same line of thinking. In this article, we will explain what root cause analysis is and how it can be used to solve problems with technological systems and applications.

Explanation of What Root-Cause Analysis Is

Because there is almost always more than one factor at play when things go wrong or when systems fail, root cause analysis is a methodical technique for pinpointing the precise causes of problems and accidents.

The notion behind root cause analysis is that in order to be a good manager, you need to do more than just react to problems as they arise. When an issue emerges, a fast band-aid solution isn’t sufficient. Also, you need to figure out how to stop them from happening in the first place, which means identifying the underlying cause of the issue.

A problem needs to exist, of course, before the root cause analysis method can be applied. You may think we’re chasing our tails, but please bear with us.

When conducting a root cause analysis, it is essential to keep in mind that you aren’t looking for a single, lone culprit. One-sided problem-solving is hampered by a narrow focus on the issue’s root cause. In addition, it prevents you from addressing the root of the issue and establishing safeguards to avoid a recurrence.

• Approaches to Identifying the Causes
• The first step is to identify the issue.
• Data pertinent to the issue should be gathered first.
• Just make sure you get a good explanation. Discover the underlying cause
• Do the right thing and take corrective measures
• Avoid further complications and future occurrences of the problem
• A solution must be put into action and tested.

The SMART rule can be used as a framework for a systematic Root Cause Analysis:

• Specific,
• Measurable,
• Action-Oriented,
• Relevant,
• Time-Bound.

Then we can utilize the Fishbone Analysis (also called the Herringbone or Ishikawa diagram) or the 5 Why analysis to identify the underlying issues.

How to Determine the “Real Cause” of a Software Problem

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What to Consider Before You Start Solving the Issue: Deterministic vs. Nondeterministic Errors

As was previously indicated, we need a problem to occur before we can investigate its origins. This necessitates our verification of the problem and the provision of methods for its reproduction. It could be difficult even if we have all the data we need from locations like error messages, stack traces, logs, and feedback from testers or end users. Tools like RevDeBug are helpful in this regard.

Different types of software issues can be classified as either deterministic or non-deterministic. The first entails our ability to not only verify that something is broken, but also describe when and why it is broken, and provide detailed instructions for reproducing the issue. Now that we have this information, we can investigate the cause of the problem rather than just its symptoms.

Errors with a lack of predictability are often more serious. To our eyes, mistakes occur at random. More information and new methods are required to fully grasp them. Although we can provide a solution for non-deterministic errors, we cannot guarantee that correcting a single line of code won’t cause further issues down the line or even elsewhere in the program.

If we can’t immediately identify determinism in a problem, we nonetheless try to find it.

Gather the information, and  set a context in which it can be used: code version, variables, logs, configuration

If you want to do a useful root cause analysis, context is key. Converting non-deterministic scenarios into deterministic ones requires us to pay attention to things like logs, variables, error messages, and which code version runs in which environment.

What are the most intractable issues, exactly? Those that we don’t keep adequate records of or keep close enough tabs on. So when mistakes are made, we increase the number of logs, producing more information from which to draw. It’s a bit like the chicken-and-egg dilemma in that we only start recording when something goes wrong, yet we don’t log nearly enough to accurately report on issues.

When we lack data that can define the scope of our problem, we have to delve even more to get to the bottom of things. You can’t ask the correct questions or separate symptoms from causes if you don’t have all the facts.

Why? Debugging, Testing, and Deploying

An issue seems obvious from the user’s point of view: the program isn’t functioning as expected. In principle, it seems easy as well. Reading the code with all of the error details, logs, variables, and the full context at your disposal should make it clear why your program crashed.

In fact, though, just one inaccuracy might set engineers on a protracted hunt to address issues like:

• Is this a localized issue, or is it affecting everyone?
• To what extent does this problem affect only some people or relate to some niche group of data (setting)?
• How common are instances like this?
• Is this related to anything we’ve done lately (like releasing new software), we ask?
• The more information we have, the closer we are to identifying the true underlying problem. So what happens if we keep looking and never seem to come up with any results?

To figure out why certain code isn’t behaving as expected, we resort to more advanced techniques like debugging. It is commonly associated with further testing, replicating conditions, or trying to replicate. A major drawback of this strategy is the lack of a comprehensive guide that can be read or accessed online. Finding the underlying cause of a problem in a complex system can be challenging or even impossible, even with expert knowledge. When no progress is made, the procedure is tedious and disheartening.

We try to put limits on non-deterministic mistakes so that they can be predicted with some degree of accuracy. To figure out what’s going on when an issue can’t be replicated, we try other solutions, wait for a failure in production, or repeat until the problem arises again.

Time is the most difficult obstacle to overcome when trying to determine the “real root cause” of software failures. If something is broken, we want it fixed immediately. We get into sticky situations when we don’t know what to do to remedy things, and so we make educated guesses.

Root Cause Analysis Goes Beyond Simply Treating Symptoms

However, while symptoms are often indicative of underlying problems, this is not always the case. Some blunders present themselves as elementary, but they lurk behind the surface of complexity. Consider, for instance, a microservice-based program or a distributed system. The procedure was supposed to conclude with an email being sent, but it didn’t.

Possible explanations include the following:

• The sending code wasn’t functioning properly.
• The email provider is to blame.
• In the absence of material, we opted not to transmit the message.

You presumably already know that we’re searching for a needle in a haystack, which is a fruitless endeavor. Today’s sophisticated, frequently cloud-based technologies provide us little room for direct oversight of our software. This means we can no longer attribute errors and their subsequent consequences to their original locations.

Finding the actual causes can be difficult without the right monitoring tools and the software development life cycle procedures in place. You might get lucky in most situations, but the ones we didn’t catch, even with our limited QA approach, will drive you crazy.

Keep in mind the statistical tenet that “correlation does not imply causation.” In order to effectively deliver solutions, it is crucial to understand the difference between causes and symptoms.

Find the answers and the right moves to make:

• If we’re going to provide a solution for the detected problem, we need to give equal weight to both resolving the current occurrence and preventing similar ones in the future. We’ve all dealt with “hotfixes” that linger on indefinitely and add to technical debt.
• You can’t be sure that the software won’t break again after you fix a single line of code if you don’t first determine what caused the issue in the first place. It also implies that the underlying issue may be buried under a mountain of technical debt, making it appear as though fixing what you think is faulty is the best option.
• Consider the case when your automated tests suddenly begin failing at random with no discernible cause. Do you know what test flakiness is? It’s when your test fails randomly in a non-deterministic environment and you need to evaluate both your code and test degradation.
• Modifying the tests will eliminate the improbability of the failure, but will this actually resolve the underlying problem? Our research shows that twenty-five percent of these unexpectedly failed tests lead to serious, difficult-to-resolve issues in production.
• Your code accumulates technical debt every time you fix a bug for the wrong reasons. During root cause analysis, we look for the underlying causes of a problem so that we can eliminate it and stop it from happening again. Problems can’t be fixed unless we know what’s causing them, and that’s a tough element of software development.

Solution Implementation and Testing

When you look back at your previous fixes, you may find that most of them were little and quick. You probably spent the vast majority of your effort thinking about where to put them. If you perform a root cause analysis in the right way, you should be able to find the solutions you need.

The main risk here is that you can’t replicate the problem in test conditions to ensure your patch actually works. If you’re operating on a modest scale, you shouldn’t have to worry about it. However, when managing larger, more complicated environments with numerous dependent systems and multiple configurations, it can be challenging to get everything set up properly to mimic the conditions in which your code will be tested.

Conclusion

Problem-solving relies heavily on establishing a context. If you don’t have it, you’ll waste time trying to determine the causes of your problems. A thorough root cause investigation requires not only a wide net but also a deep dive into the many parts of the code.

Unfortunately, not all of the information required for effective error root cause analysis is provided by the current crop of error monitoring and APM technologies. As a result, there is a reliance on a “few chosen groups of persons” who possess an in-depth understanding of the systems, which limits their accessibility and hinders examination.

The tools we use for creating software have advanced significantly in recent decades, but the process of fixing bugs has changed little in that time. Businesses constantly seek out new and improved technologies to assist them quickly and accurately in determining the True Root Cause of application problems.

When it comes to visualizing failures and providing 100% reproduction for each error, RevDeBug is the only solution that can do it for development teams. The entire software delivery lifecycle can benefit from the rapid True Root Cause investigation this enables every hiccup and slowness. To find out more, check out our webpage.

About Enteros

​​Enteros offers a patented database performance management SaaS platform. It proactively identifies root causes of complex business-impacting database scalability and performance issues across a growing number of clouds, RDBMS, NoSQL, and machine learning database platforms.